Text quality, however, is not equal to explainbility.
In the case of explainable recommendation, users may value more an explanation that justifies a recommendation's advantages on certain features [1]}, [2]}.
To this end, we adopt the other three metrics proposed by [1]}: Feature Matching Ratio (FMR), Feature Coverage Ratio (FCR) and Feature Diversity (DIV).
FMR measures whether a generated explanation contains the feature in the ground-truth.
FCR is computed as the number of distinct features contained in all the generated explanations, divided by the total number of features in the whole dataset.
DIV measures the intersection of features between any two generated explanations.
